Interpretation of food preference using binary food logging data.

Creation of an indexer with tf-idf weights and a ranking system

University of Washington MOOC | Practical case-studies from regression and classification to deep learning and recommender systems

Read all text files under a directory, and generate the TF-IDF scores of each term in each file to a output file

A GUI implementation for searching song and saving lyrics in a text file.

Sublime Text 3 Package for searching all documents in a project using tf-idf

Repository for Information Retrieval and Data Mining Coursework (2019) - Fact Extraction and Verification (FEVER)

Computing the TF-IDF socre of each Doc for a query

Different ML techniques examples

🚀 Term Frequency - Inverse Document Frequency

This project entailed an analysis of the readme text in various GitHub repositories. A partner and I, had the objective to build a classification model to predict the primary programming language a repo was using based on the content within the readme. We identified distinct and common words among repositories that were written in Python or Java…

Classifying Yelp Reviews using JohnSnowLab

Content-based Recommender System with Natural Language Processing using TF-IDF Vectorizer, Count Vectorizer and KNN.

Trabalho Prático 02 da disciplina de Sistemas de Recomendação.

This project aims to create an outline of a tool which could be used as an improved form of search engine for AirBnB style listings. By taking into account purely tf-idf across listings with a minimum number of contributions, this exploratory demonstration seeks to exhibit how one might find more specific or better fitting for specific tastes vs…

Anime Recommender System with Content-based and Collaborative Filtering

Three different methods namely TFIDF, word average embedding method and inverse document frequency method were used to build a text matching system. The systems were tested on the first 100 questions which were duplicate. A maximum accuracy score of 77% and 67% in top5 and top 2 matches was obtained using average word model.